1. Field of the Invention
The invention relates to bioinformatics and, more particularly, to formulating disease diagnoses from clinical test data.
2. Description of the Related Art
In the area of disease diagnosis and detection, clinical tests are used to obtain data regarding a patient. The clinical tests yield a large volume of data, including patient symptoms and test results, as well as patient characteristics, such as age, gender, geographic location, and weight. The data can vary depending on the progression of a particular disease and when the clinical tests are conducted on a patient. The amount of clinical test data available is growing larger as additional tests are performed on an increasing number of patients.
The multitude of clinical test data that is available does not necessarily lead to an improvement in disease diagnosis for a patient. Indeed, the opposite can be true, as the volume of clinical test data and the high dimensionality of such data lead to a large quantity of possible diagnoses that can result from the data. A single patient can have multiple diagnoses that could result from the same data set. Additionally, the data can contain patterns that are not readily apparent or could contain information related to diseases that are not commonly diagnosed, difficult to diagnose, or for which a diagnostic test is not available or does not exist. This can lead to an inefficient use of clinical data wherein the analysis of the data leads to improper diagnoses or to missed diagnoses due to a failure to spot patterns or connections in the data.
In view of the foregoing, it should be apparent that there is a need for a method of mining and analyzing clinical test result data in connection with disease diagnosis. The present invention fulfills this need.